I'd like to create an FsCheck Generator to generate instances of a "complex" object. By complex, I mean an existing class in C# that has a number of child properties and collections. These properties and collections in turn need to have data generated for them.
Imagine this class was named Menu with child collections Dishes and Drinks (I'm making this up so ignore the crappy design). I want to do the following:
Generate a variable number of Dishes and a variable number of Drinks.
Generate the Dish and Drink instances using the FsCheck API to populate their properties.
Set some other primitive properties on the Menu instance using the FsCheck API.
How does one go about writing a generator for this type of instance? Is this a bad idea? (I'm new to property based testing). I have read the docs, but have clearly failed to internalise it all so far.
There is a nice example for generating a record, but this is really only generating 3 values of the same type float.
This is not a bad idea - in fact it's the whole point that you are able to do this. FsCheck's generators are fully compositional.
Note first that if you have immutable objects whose constructors take primitive types, like your Drink and Dish looks like, FsCheck can generate these out of the box (using reflection)
let drinkArb = Arb.from<Drink>
let dishArb = Arb.from<Dish>
should give you an Arbitrary instance, which is a generator (generates a random Drink instance) and a shrinker (takes a Drink instance and makes it 'smaller' - this helps with debugging, esp. for composite structures, where you get a small counter-example if your test fails).
This breaks down fairly quickly though - in your example you probably don't want negative integers for the number of drinks or the number of dishes. The above code will generate negative numbers though. Sometimes this is easy to fix if your type is really just a wrapper of some sort around another type, using Arb.convert, e.g.
let drinksArb = Arb.Default.PositiveInt() |> Arb.convert (fun positive -> new Drinks(positive) (fun drinks -> drinks.Amount)
You need to provide to and from conversions to Arb.convert and presto, new arbitrary instance for Drinks that maintains your invariant. Other invariants may not be so easy to maintain of course.
After that it becomes a bit harder to generate a generator and a shrinker at the same time from those two pieces. Always start with the generator, then shrinker comes later if (when) you need it. #simonhdickson's example looks reasonable. If you have the arbitrary instances above, you can get at their generator by calling .Generator.
let drinksGen = drinksArb.Generator
Once you have the parts generators (Drink and Dish), you can indeed compose them together as #simonhdickson proposes:
let menuGenerator =
Gen.map3 (fun a b c -> Menu(a,b,c)) (Gen.listOf dishGenerator) (Gen.listOf drinkGenerator) (Arb.generate<int>)
Divide and conquer! Overall have a look at what intellisense on Gen gives you to get some ideas of how to compose generators.
There might be a better way of describing this, but I think this might do what you're thinking of. Each of the Drink/Dish types could take further parameters using the same kind of style as the menuGenerator does
type Drink() =
member m.X = 1
type Dish() =
member m.Y = 2
type Menu(dishes:Dish list, drinks:Drink list, total:int) =
member m.Dishes = dishes
member m.Drinks = drinks
member m.Total = total
let drinkGenerator = Arb.generate<unit> |> Gen.map (fun () -> Drink())
let dishGenerator = Arb.generate<unit> |> Gen.map (fun () -> Dish())
let menuGenerator =
Gen.map3 (fun a b c -> Menu(a,b,c)) <| Gen.listOf dishGenerator <| Gen.listOf drinkGenerator <| Arb.generate<int>
Related
I seem to often run into cases where I want to generate some complex structure, but a special variation with a member type generated differently.
For example, consider this tree
type Tree<'LeafData,'INodeData> =
| LeafNode of 'LeafData
| InternalNode of 'INodeData * Tree<'LeafData,'INodeData> list
I want to generate cases like
No internal node is childless
There are no leaf-type nodes
Only a limited subset of leaf types are used
These are simple to do if I override all generation of a corresponding child type.
The problem is that it seems register is inherently a thread-level action, and there is no gen-local alternative.
For example, what I want could look like
let limitedLeafs =
gen {
let leafGen = Arb.generate<LeafType> |> Gen.filter isAllowedLeaf
do! registerContextualArb (leafGen |> Arb.fromGen)
return! Arb.generate<Tree<NodeType, LeafType>>
}
This Tree example specifically can work around with some creative type shuffling, but that's not always possible.
It's also possible to use some sort of recursive map that enforces assumptions, but that seems relatively complex if the above is possible. I might be misunderstanding the nature of FsCheck generators though.
Does anyone know how to accomplish this kind of gen-local override?
There's a few options here - I'm assuming you're on FsCheck 2.x but keep scrolling for an option in FsCheck 3.
The first is the most natural one but is more work, which is to break down the generator explicitly to the level you need, and then put humpty dumpty together again. I.e don't rely on the type-based generator derivation so much - if I understand your example correctly that would mean implementing a recursive generator - relying on Arb.generate<LeafType> for the generic types.
Second option - Config has an Arbitrary field which you can use to override Arbitrary instances. These overrides will take effect even if the overridden types are part of the automatically generated ones. So as a sketch you could try:
Check.One ({Config.Quick with Arbitrary = [| typeof<MyLeafArbitrary>) |]) (fun safeTree -> ...)
More extensive example which uses FsCheck.Xunit's PropertyAttribute but the principle is the same, set on the Config instead.
Final option! :) In FsCheck 3 (prerelease) you can configure this via a new (as of yet undocumented) concept ArbMap which makes the map from type to Arbitrary instance explicit, instead of this static global nonsense in 2.x (my bad of course. seemed like a good idea at the time.) The implementation is here which may not tell you all that much - the idea is that you put an ArbMap instance together which contains your "safe" generators for the subparts, then you ArbMap.mergeWith that safe map with ArbMap.defaults (thus overriding the default generators with your safe ones, in the resulting ArbMap) and then you use ArbMap.arbitrary or ArbMap.generate with the resulting map.
Sorry for the long winded explanation - but all in all that should give you the best of both worlds - you can reuse the generic union type generator in FsCheck, while surgically overriding certain types in that context.
FsCheck guidance on this is:
To define a generator that generates a subset of the normal range of values for an existing type, say all the even ints, it makes properties more readable if you define a single-case union case, and register a generator for the new type:
As an example, they suggest you could define arbitrary even integers like this:
type EvenInt = EvenInt of int with
static member op_Explicit(EvenInt i) = i
type ArbitraryModifiers =
static member EvenInt() =
Arb.from<int>
|> Arb.filter (fun i -> i % 2 = 0)
|> Arb.convert EvenInt int
Arb.register<ArbitraryModifiers>() |> ignore
You could then generate and test trees whose leaves are even integers like this:
let ``leaves are even`` (tree : Tree<EvenInt, string>) =
let rec leaves = function
| LeafNode leaf -> [leaf]
| InternalNode (_, children) ->
children |> List.collect leaves
leaves tree
|> Seq.forall (fun (EvenInt leaf) ->
leaf % 2 = 0)
Check.Quick ``leaves are even`` // output: Ok, passed 100 tests.
To be honest, I like your idea of a "gen-local override" better, but I don't think FsCheck supports it.
I'm pretty new to functional programming so this might be a question due to misconception, but I can't get my head around this - from an OOP point of view it seems so obvious...
scenario:
Assume you have an actor or micro-service like architecture approach where messages/requests are sent to some components that handle them and reply. Assume now, one of the components stores some of the data from the requests for future requests (e.g. it calculates a value and stores it in a cache so that the next time the same request occurs, no calculation is needed).
The data can be hold in memory.
question:
How do you in functional programming in general, and especially in f#, handle such a scenario? I guess a static dictionary is not a functional approach and I don't want to include any external things like data stores if possible.
Or more precise:
If an application creates data that will be used later in the processing again, where do we store the data?
example: You have an application that executes some sort of tasks on some initial data. First, you store the inital data (e.g. add it to a dictionary), then you execute the first task that does some processing based on a subset of the data, then you execute the second task that adds additional data and so on until all tasks are done...
Now the basic approach (from my understanding) would be to define the data and use the tasks as some sort of processing-chain that forward the processed data, like initial-data -> task-1 -> task-2 -> ... -> done
but that does not fit an architecture where getting/adding data is done message-based and asynchronous.
approach:
My initial approach was this
type Record = { }
let private dummyStore = new System.Collections.Concurrent.ConcurrentBag<Record>()
let search comparison =
let matchingRecords = dummyStore |> Seq.where (comparison)
if matchingRecords |> Seq.isEmpty
then EmptyFailedRequest
else Record (matchingRecords |> Seq.head)
let initialize initialData =
initialData |> Seq.iter (dummyStore.Add)
let add newRecord =
dummyStore.Add(newRecord)
encapsulated in a module that looks to me like an OOP approach.
After #Gustavo asked me to provide an example and considering his suggestion I've realized that I could do it like this (go one level higher to the place where the functions are actually called):
let handleMessage message store =
// all the operations from above but now with Seq<Record> -> ... -> Seq<Record>
store
let agent = MailboxProcessor.Start(fun inbox->
let rec messageLoop store = async{
let! msg = inbox.Receive()
let modifiedStore = handleMessage msg store
return! messageLoop modifiedStore
}
messageLoop Seq.empty
)
This answers the question for me well since it removed mutability and shared state at all. But when just looking at the first approach, I cannot think of any solution w/o the collection outside the functions
Please note that this question is in f# to explain the environment, the syntax etc. I don't want a solution that works because f# is multi-paradigm, I would like to get a functional approach for that.
I've read all questions that I could find on SO so far but they either prove the theoretical possibility or they use collections for this scenario - if duplicated please point me the right direction.
You can use a technique called memoization which is very common in FP.
And it consists precisely on keeping a dictionary with the calculated values.
Here's a sample implementation:
open System
open System.Collections.Concurrent
let getOrAdd (a:ConcurrentDictionary<'A,'B>) (b:_->_) k = a.GetOrAdd(k, b)
let memoize f =
let dic = new ConcurrentDictionary<_,_>()
getOrAdd dic f
Note that with memoize you can decorate any function and get a memoized version of it. Here's a sample:
let f x =
printfn "calculating f (%i)" x
2 * x
let g = memoize f // g is the memoized version of f
// test
> g 5 ;;
calculating f (5)
val it : int = 10
> g 5 ;;
val it : int = 10
You can see that in the second execution the value was not calculated.
I have an object that can be neatly described by a discriminated union. The tree that it represents has some properties that can be easily updated when the tree is modified (but remaining immutable) but that are relatively expensive to recalculate.
I would like to store those properties along with the object as cached values but I don't want to put them into each of the discriminated union cases so I figured a member variable would fit here.
The question is then, how do I change the member value (when I modify the tree) without mutating the actual object? I know I could modify the tree and then mutate that copy without ruining purity but that seems like a wrong way to go about it to me. It would make sense to me if there was some predefined way to change a property but so that the result of the operation is a new object with that property changed.
To clarify, when I say modify I mean doing it in a functional way. Like (::) "appends" to the beginning of a list. I'm not sure what the correct terminology is here.
F# actually has syntax for copy and update records.
The syntax looks like this:
let myRecord3 = { myRecord2 with Y = 100; Z = 2 }
(example from the MSDN records page - http://msdn.microsoft.com/en-us/library/dd233184.aspx).
This allows the record type to be immutable, and for large parts of it to be preserved, whilst only a small part is updated.
The cleanest way to go about it would really be to carry the 'cached' value attached to the DU (as part of the case) in one way or another. I could think of several ways to implement this, I'll just give you one, where there are separate cases for the cached and non-cached modes:
type Fraction =
| Frac of int * int
| CachedFrac of (int * int) * decimal
member this.AsFrac =
match this with
| Frac _ -> this
| CachedFrac (tup, _) -> Frac tup
An entirely different option would be to keep the cached values in a separate dictionary, this is something that makes sense if all you want to do is save some time recalculating them.
module FracCache =
let cache = System.Collections.Generic.Dictionary<Fraction, decimal>()
let modify (oldFrac: Fraction) (newFrac: Fraction) =
cache.[newFrac] <- cache.[oldFrac] + 1 // need to check if oldFrac has a cached value as well.
Basically what memoize would give you plus you have more control over it.
Given the following two approaches, what would the cons and pros of both, when it comes to function composition?
Approach 1
let isNameTaken source name =
source |> Query.Exists(fun z -> z.Name = name)
let usage : Customer = isNameTaken source "Test"
Approach 2
let isNameTaken f name =
f(fun z -> z.Name = name)
let usage : Customer = isNameTaken (source |> Query.Exists) "Test"
Is it just silly to pass (source |> Query.Exists) in Approach 2 - is it too extreme?
It depends on the wider context. I would generally prefer the first approach, unless you have some really good reason for using the second style (e.g. there is a number of functions similar to Query.Exists that you need to apply in a similar style).
Aside - I think your second example has a couple of issues (e.g. the piping in source |> Query.Exists would have to be replaced with (fun pred -> source |> Query.Exists pred) which makes it uglier.
Even then, the second approach does not really give you much benefit - your isNameTaken is simply a function that tests whether a customer name equals a given name and then it passes that as an argument to some f - you could just define a function that tests name equality and write something like this:
let nameEquals name (customer:Customer) =
customer.Name = name
let usage = source |> Query.Exists (nameEquals "Test")
More generally, I think it is always preferable to write code so that the caller can compose the pieces that are available to them (like Query.Exists, nameEquals etc.) rather than In a way that requires the caller to fill some holes of a particular required shape (e.g. implement a function with specified signature).
I think the answer to your question has to do with two main criteria. Which is more important, the readability of the code or the decoupling of the query from isNameTaken. In this particular case, I'm not sure that you get much at all from decoupling the query and it also seems like your decoupling is partial.
The thing I don't like about this is that in both cases, you've got z.Name tightly coupled into isNameTaken, which means that isNameTaken needs to know about the type of z. If that's OK with you then fine.
This is a pretty simple question, and I just wanted to check that what I'm doing and how I'm interpreting the F# makes sense. If I have the statement
let printRandom =
x = MyApplication.getRandom()
printfn "%d" x
x
Instead of creating printRandom as a function, F# runs it once and then assigns it a value. So, now, when I call printRandom, instead of getting a new random value and printing it, I simply get whatever was returned the first time. I can get around this my defining it as such:
let printRandom() =
x = MyApplication.getRandom()
printfn "%d" x
x
Is this the proper way to draw this distinction between parameter-less functions and values? This seems less than ideal to me. Does it have consequences in currying, composition, etc?
The right way to look at this is that F# has no such thing as parameter-less functions. All functions have to take a parameter, but sometimes you don't care what it is, so you use () (the singleton value of type unit). You could also make a function like this:
let printRandom unused =
x = MyApplication.getRandom()
printfn "%d" x
x
or this:
let printRandom _ =
x = MyApplication.getRandom()
printfn "%d" x
x
But () is the default way to express that you don't use the parameter. It expresses that fact to the caller, because the type is unit -> int not 'a -> int; as well as to the reader, because the call site is printRandom () not printRandom "unused".
Currying and composition do in fact rely on the fact that all functions take one parameter and return one value.
The most common way to write calls with unit, by the way, is with a space, especially in the non .NET relatives of F# like Caml, SML and Haskell. That's because () is a singleton value, not a syntactic thing like it is in C#.
Your analysis is correct.
The first instance defines a value and not a function. I admit this caught me a few times when I started with F# as well. Coming from C# it seems very natural that an assignment expression which contains multiple statements must be a lambda and hence delay evaluated.
This is just not the case in F#. Statements can be almost arbitrarily nested (and it rocks for having locally scoped functions and values). Once you get comfortable with this you start to see it as an advantage as you can create functions and continuations which are inaccessible to the rest of the function.
The second approach is the standard way for creating a function which logically takes no arguments. I don't know the precise terminology the F# team would use for this declaration though (perhaps a function taking a single argument of type unit). So I can't really comment on how it would affect currying.
Is this the proper way to draw this
distinction between parameter-less
functions and values? This seems less
than ideal to me. Does it have
consequences in currying, composition,
etc?
Yes, what you describe is correct.
For what its worth, it has a very interesting consequence able to partially evaluate functions on declaration. Compare these two functions:
// val contains : string -> bool
let contains =
let people = set ["Juliet"; "Joe"; "Bob"; "Jack"]
fun person -> people.Contains(person)
// val contains2 : string -> bool
let contains2 person =
let people = set ["Juliet"; "Joe"; "Bob"; "Jack"]
people.Contains(person)
Both functions produce identical results, contains creates its people set on declaration and reuses it, whereas contains2 creates its people set everytime you call the function. End result: contains is slightly faster. So knowing the distinction here can help you write faster code.
Assignment bodies looking like function bodies have cought a few programmers unaware. You can make things even more interesting by having the assignment return a function:
let foo =
printfn "This runs at startup"
(fun () -> printfn "This runs every time you call foo ()")
I just wrote a blog post about it at http://blog.wezeku.com/2010/08/23/values-functions-and-a-bit-of-both/.